Receiver Parametric Covariance Estimation for Precoded MIMO Transmissions

ABSTRACT

A model-based technique for estimating signal impairments that can accommodate various transmitted signal configurations, including closed-loop transmit diversity signals and pre-coded MIMO signals, is disclosed. In an exemplary method, an impairment model is constructed for a received composite information signal comprising at least a first data stream transmitted from first and second antennas according to a first antenna weighting vector. The impairment model includes one or more model terms scaled by corresponding scaling parameters, wherein the model terms capture propagation channel effects and are independent of the first antenna weighting vector, and wherein the scaling parameters capture effects of the first antenna weighting vector. A parametric estimate of the impairment covariance is calculated using the impairment model.

FIELD OF THE INVENTION

The present invention relates generally to wireless communicationsystems, and in particular to a parametric system and method forcovariance estimation in a wireless communication system employingprecoded multiple-input multiple-output (MIMO) transmissions.

BACKGROUND

Spread-spectrum communication systems are well known in the art andwidely deployed. A class of receivers well suited for use inspread-spectrum systems—such as those standardized in IS-95, IS-2000(cdma2000), and the 3^(rd)-Generation Partnership Project's (3GPP)Wideband Code-Division Multiple Access (W-CDMA) specifications—is thelinear interference-whitening (LIW) receiver. LIW receivers suppressinterference in addition to collecting signal energy for detection. Oneform of the LIW receiver is a transversal chip equalizer; another is aG-Rake receiver. The Rake receiver derives its name from its rake-likestructure, wherein multiple receiver “fingers” are used to receivemultiple signal images in a received multipath signal. By coherentlycombining the finger outputs in a weighted Rake combiner, theconventional Rake receiver can use multipath reception to improve theSignal to Interference-plus-Noise Ratio (SINR) of the received signal. AGeneralized Rake (G-Rake) receiver improves interference suppressionperformance over a conventional Rake receiver using more sophisticatedgeneration of the combining weights.

Recently, 2×2 Multiple-Input Multiple-Output (MIMO) technology has beenstandardized in Release 7 of the 3GPP specifications. The standardizedscheme, referred to as Dual-Transmit Adaptive Arrays (D-TxAA), issimilar to selective per-antenna rate control (S-PARC), except thatadaptive unitary precoding is applied to each of the data streams, inthis case to each of one or two High-Speed Downlink Shared Channel(HS-DSCH) data streams.

D-TxAA can be viewed as an extension of the previously standardizedclosed-loop mode-1 (CL-1) transmit diversity scheme, in that theprecoding vectors (which map a data stream to the multiple transmitantennas) used for each of the D-TxAA data streams are selected from thesame codebook used for CL-1. In contrast to CL-1, however, D-TxAAincludes two modes of operation—single-stream mode and dual-stream mode.In single-stream mode, one of the four possible preceding vectors fromthe CL-1 codebook is applied to a single data stream. In dual-streammode, orthogonal pairs of preceding vectors (again selected from theCL-1 codebook) are applied to the two data streams. The use of precodinghas a significant impact on the receiver, and in particular complicatesthe design of LIW receivers such as Rake receivers.

Earlier versions of the 3GPP W-CDMA specifications (i.e., prior toRelease 7) define two transmit diversity modes: CL-1, and an open-loopmode known as STTD. U.S. patent application Ser. No. 10/800,167 (Pub.No. US 2005/0201447), titled “Method and Apparatus for ParameterEstimation in a Generalized Rake Receiver,” filed Mar. 12, 2004 byCairns et al. (the “Cairns application”), assigned to the assignee ofthe present application and incorporated herein by reference in itsentirety, discloses a solution for G-Rake receivers in a transmitdiversity system. The solution describes a parametric approach toestimating an impairment covariance matrix used to form G-Rake combiningweights. The parametric approach estimates the impairment covariance asa sum of terms, including a separate term for each transmit antenna aswell as a term corresponding to the sum of noise plus other-cellinterference.

This solution works well for open-loop transmit diversity modes. In anopen-loop mode, the impairments corresponding to each transmit antennaduring a particular symbol period are uncorrelated, since differentsymbols are transmitted from the different antennas. In closed-loopmode, however, the mobile terminal specifies a phase offset, and thesame symbol is transmitted by a primary antenna and simultaneously by asecondary antenna with the specified phase offset. In this case, theimpairment due to each transmit antenna is highly correlated. Thiscorrelation may be exploited to improve interference suppression andreceiver performance. U.S. patent application Ser. No. 11/751,109,titled “Receiver Parametric Covariance Estimation for TransmitDiversity,” filed May 21, 2007 by Jonsson et al. (the “Jonssonapplication”), assigned to the assignee of the present application andincorporated herein by reference in its entirety, discloses a parametricapproach to estimating an impairment covariance matrix that accounts forthe simultaneous transmission of the same symbols from a first andsecond antenna. In this approach the impairment covariance matrix for asystem employing two transmit antennas is formulated as a sum of seventerms, including a term corresponding to each of the transmit antennas,a noise-plus-other-cell-interference term, plus four additional termscorresponding to the four possible precoding vectors in the CL-1codebook. The terms are weighted by fitting parameters determined byfitting the parametrically modeled impairment covariance to a measuredimpairment covariance. An implicit assumption is that if one or more ofthe preceding vectors are not used by any user in the cell, then thecorresponding fitting parameter will ideally be estimated as zero.

The CL-1 covariance estimation approach described in the Jonssonapplication applies to the transmission of only a single data stream,mapped according to a preceding vector to two (or more) antennas. Incontrast, in D-TxAA, two data streams may be transmitted simultaneously,with both data streams sharing the same set of channelization codes.This creates additional self-interference, referred to as code-reuseinterference, which affects the formulation of the impairmentcovariance. Code reuse is not accounted for in the formulation ofJonsson, since only one data stream is ever transmitted in CL-1.

Furthermore, in the solution described by Jonsson, an impairment termcorresponding to each of the four possible preceding vectors in the CL-1codebook is computed, since the receiver typically has no knowledge ofwhich precoding vectors (except its own) are utilized by thetransmitter. As mentioned above, if one or more of the preceding vectorsis not actually utilized by at least one other same-cell user, then thefitting parameter corresponding to that term should ideally be estimatedas zero. In this case, then, the impairment term is unnecessarilyconstructed. Because construction of the impairment terms iscomputationally demanding, any unnecessary construction of one or moreimpairment terms is undesirable. A related issue in a situation whereone or more of the precoding vectors are not utilized is that theimpairment covariance matrix is over-modeled, which may potentially leadto well-known problems with fitting parameter estimation and resultingpoor performance.

SUMMARY

The present invention provides methods and apparatus to estimate signalimpairment covariances for one or more received signals of interestusing a model-based technique that can accommodate various transmittedsignal configurations, including closed-loop transmit diversity signalsand pre-coded MIMO signals. According to one or more embodimentsdescribed and claimed herein, a parametric form of G-Rake and chipequalization is provided that accounts for impairment correlationbetween Rake fingers or equalizer taps. In an exemplary method, animpairment model is constructed for a received composite informationsignal comprising at least a first data stream transmitted from firstand second antennas according to a first antenna weighting vector. Theimpairment model includes one or more model terms scaled bycorresponding scaling parameters, wherein the model terms capturepropagation channel effects and are independent of the first antennaweighting vector, and wherein the scaling parameters capture effects ofthe first antenna weighting vector. A parametric estimate of theimpairment covariance is calculated using the impairment model.

Another embodiment relates to a wireless communication receiver for usein a mobile terminal in a transmit diversity wireless communicationsystem. The receiver includes a radio front-end circuit configured toprovide a received signal of interest containing at least a first datastream transmitted simultaneously from a first and second antennaaccording to a first antenna weighting vector. The receiver circuit isconfigured to carry out one or more of the methods described herein forestimating impairment covariance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a wireless communication system.

FIG. 2 is a functional block diagram of an exemplary symbol-level LIWreceiver configured to process received signals transmitted from atleast first and second transmit antennas.

FIG. 3 is a functional block diagram of an exemplary chip-level LIWreceiver configured to process received signals transmitted from atleast first and second transmit antennas.

FIG. 4 is a block diagram illustrating the composition of a transmittedsignal.

FIG. 5 is a flow diagram of an exemplary method of estimating impairmentcovariance of a received signal of interest.

FIG. 6 is a flow diagram of a method of estimating stream-specificimpairment covariances for a pre-coded information signal according toone or more embodiments of the present invention.

FIG. 7 is a flow diagram of an exemplary method for estimatingimpairment covariances for use in CQI estimation.

DETAILED DESCRIPTION

FIG. 1 depicts an exemplary wireless communication system 100 employingclosed-loop transmit diversity, such as CL-1, and/or multiple-inputmultiple-output (MIMO) transmissions, such as according to the D-TxAAspecifications. Within a Radio Access Network (RAN) 102, a Radio NetworkController (RNC) 104 controls a plurality of base transceiver stations(BTS) 106, also known in the art as Node B's. Each Node B 106 providesradio communication services with subscriber mobile terminals 112 withina geographic area called a cell, which may be divided into sectors, asdepicted in FIG. 1. The RNC 104 communicates with a Core Network (CN)114, which in turn is connected to one or more external networks 116,such as the Public Switched Telephone Network (PSTN), the Internet, orthe like.

Embodiments of the present invention are described herein with respectto WCDMA standards, including the CL-1 specifications and specificationsfor D-TxAA, which is more fully described below. However, the inventionis not so limited, and the inventive concepts disclosed and claimedherein may be advantageously applied to a wide array of transmitdiversity systems.

Each base station 106 includes at least a primary transmit antenna 108and a secondary transmit antenna 110 (either per-cell or per-sector,depending on the network 100 configuration), as shown in FIG. 2. Thebase station 106 may transmit an information signal, such as a precodedvoice signal or a precoded High-Speed Downlink Packet Access (HSDPA)data signal using both antennas 108, 110. The signal transmitted on thesecondary antenna 110 is weighted relative to the signal transmitted onthe primary antenna 108, wherein the transmit weights may comprise phaseoffset only, or may more generally comprise a complex quantity havingboth phase and amplitude. The phase shift employed may be determined byfeedback from the mobile terminal 112, thus forming a closed-looptransmit diversity system.

As one non-limiting example, in the WCDMA standard known as CL-1, therelative phases (θ_(i)) of the secondary antenna are 45, 135, 225, or315 degrees, or

$\theta_{i} = {\pi ( {{- \frac{1}{4}} + \frac{i}{2}} )}$

for i=1, 2, 3, 4 in radians. In general, the two transmit antennas canhave different filters applied, which can introduce different phase,amplitude, and delay characteristics. Usually single-tap filters areemployed with a common delay, so that the two transmit antennas arecharacterized by different complex antenna weight values.

In addition to an information signal, the base station transmits a pilotchannel from each transmit antenna 108, 110. The pilot channels comprisea series of pilot symbols. In some embodiments, the primary transmitantenna 108 transmits a sequence of QPSK pilot symbols s_(p)(k) using alinked-256 Walsh code that is scrambled by a specific long code. In theWCDMA standard, the pilot symbols take on the same values_(p)(k)=(1+j)/√{square root over (2)}. The sample may be treated aspurely real or purely imaginary, as described in U.S. Pat. No.6,005,887, DESPREADING OF DIRECT SEQUENCE SPREAD SPECTRUM COMMUNICATIONSSIGNALS, issued Dec. 21, 1999 to Bottomley, et al., assigned to theassignee of the present application, and incorporated herein byreference in its entirety. For the secondary transmit antenna 110, thesame Walsh code and the same scrambling code are used. However, thepilot symbol values are modified by a sequence M_(k) such thatM_(k)s_(p)(k) is sent. For example, the sequence M_(k) in slot 0 ofevery frame is given by

{M _(k) ;k=1,10}=+1,−1,−1,+1,+1,−1,−1,+1,+1,−1.

Thus, over the duration of each two symbol periods (512 chips), thepilot signals from the primary antenna 108 and the secondary antenna 110are orthogonal. This can be viewed as two pilot channels usingsupersymbols of length 512 (referred to as nonoverlapping symbolperiods). By contrast, each set of 256 chips corresponds to anoverlapping pilot symbol period.

In general, the base station power allocated to the pilots on theprimary and secondary antennas could be different. To reflect this, thepower allocation parameters γ_(p)(0) and γ_(p)(1) are introduced. Thesequantities take values between 0 and 1 that represent the fraction ofthe total pilot power allocated to the pilots on the primary andsecondary antenna, respectively. For the special case of equal pilotpower allocation, γ_(p)(0)=γ_(p)(1)=0.5.

There are two main types of LIW receiver architectures. One usessymbol-level equalization, which is typically based on maximumlikelihood estimation techniques. This type of receiver includes theG-Rake receiver 200, illustrated in block diagram form in FIG. 2. Aradio processor 202 generates chip samples from a received signal, whichincludes the information signal transmitted from antennas 108 and 110 atbase station 106. The chip samples are provided to a finger placementcircuit 204, which determines the “finger delays,” usually includingmultipath delays, used to despread a received CDMA signal in acorrelation unit 206. The finger delays are also provided to a weightcomputer 208 that computes combining weights which are used to combinethe despread values in a combiner 210 to produce soft values, orestimates of the symbol values.

Another type of LIW receiver is a chip-level equalizer, which typicallyincludes Minimum Mean-Square Error (MMSE) transversal chip equalization300, as illustrated in block diagram form in FIG. 3. A radio processor302 generates chip samples from a received signal. The chip samples areprovided to a tap placement circuit 304, which determines the tapdelays, related to multipath delays, for a Finite Impulse Response (FIR)filter 306. The selected tap delays are also provided to a weightcalculator 308 that computes filter coefficients (or weights) for theFIR filter 306. The FIR filter 306 filters the chip samples to produce asignal that is despread by a correlator 310 to produce symbol estimates.

Both types of LIW receivers 200, 300 rely on an estimate of a covariancematrix. In the case of maximum likelihood G-Rake processing, thecovariance matrix is an impairment covariance matrix. In the case ofMMSE-based processing, a data covariance matrix, which is closelyrelated to the impairment covariance matrix, is used. According toembodiments described herein, an impairment model is constructed, theimpairment model generally including several model terms scaled bycorresponding scaling parameters. As will be shown below, the modelterms may be constructed so that each is independent of antennaweighting vectors (preceding vectors) used for transmitting theinformation signals. Effects of the antenna weighting vectors arecaptured by the scaling parameters. The scaling parameters may becalculated, in some embodiments, or may be jointly estimated (“fitted”)according to well-known techniques by fitting the impairment model tomeasured impairment covariance or data covariance. For convenience, thisprocess is described with respect to a G-Rake receiver and an impairmentcovariance matrix. However, the use of a data covariance matrix in thecase of a chip equalizer is directly analogous.

In order to provide context for a detailed discussion of impairmentmodels, additional background of the D-TxAA MIMO scheme standardized by3GPP in Release-7 High-Speed Packet Access (HSPA) specifications isprovided here. A high-level view of the scheme is shown in FIG. 4. InRelease-6 (non-MIMO) HSPA, the theoretical maximum downlink data rate is14.4 Mbps which is achieved using 15 codes, 16-QAM, and coding rate 1.With D-TxAA, this peak rate may doubled to 28.8 Mbps by transmitting asecond, separately encoded data stream in parallel, when channelconditions warrant. Typically, this occurs at high signal-to-noiseratios (SNRs) and when the channel is full rank. In rank-deficientscenarios and/or lower SNRs, the second data stream may be switched off,as indicated in the figure, so that only a single data HSPA data streamis transmitted.

D-TxAA uses a form of unitary precoding applied to the HS-DSCH datastreams using the precoding weight matrix B. The weight vectors (columnsof B) applied to each stream are drawn from the same codebook of fourphase-only weights used for the closed-loop mode-1 (CL-1) transmitdiversity option defined in Rel-99:

$\begin{matrix}{{u_{i} = {\frac{1}{\sqrt{2}}\begin{bmatrix}1 \\^{j\; \frac{\pi}{4}{({{2i} - 1})}}\end{bmatrix}}},\mspace{14mu} {i = 1},2,3,4.} & (1)\end{matrix}$

Those skilled in the art will appreciate that while phase-only weightsare used in D-TxAA as currently standardized, MIMO transmission moregenerally may use amplitude weighting as well. Further, the codebookfrom which the weight vectors are drawn is by no means limited to fourentries.

In any event, for the case of single-stream D-TxAA transmission, theweight vector is a single one of the four possible vectors in the CL-1codebook. For the case of dual-stream transmission, the two used weightvectors are selected to be orthogonal; hence, B is unitary. With respectto the CL-1 codebook as defined in Equation (1), the orthogonal pairingsare thus (1,3) and (2,4) and permutations thereof.

With this structure, there are fundamentally eight different transmittermodes corresponding to the different choices for B and the differentmodes—4 single-stream modes and 4 dual-stream modes. The single-streammodes are defined by

B∈{[u₁ 0],[u₂ 0],[u₃ 0],[u₄ 0]}  (2)

and the dual-stream modes by

B∈{[u₁ u₃],[u₂ u₄],[u₃ u₁],[u₄ u₂]}.   (3)

A selection of the “best” transmitter mode is made by the user equipment(UE) through maximizing some metric such as data rate, SINR, receivedpower per-stream, or the like. The UE indicates its preferred precodingconfiguration through feedback, called preceding control information(PCI), transmitted to the Node B on the high-speed dedicated physicalcontrol channel (HS-DPCCH), as shown in FIG. 4. The PCI feedback 405informs the scheduler in the Node B of the preferred number of datastreams and the preferred preceding weight vector(s). The Node B signalsthe actually used preceding matrix B, as well as the actuallytransmitted number of streams, on the downlink high-speed shared controlchannel (HS-SCCH). This information is used to configure the receiver inthe UE.

As can be seen in FIG. 4, a number of different signals form thecomposite transmitted signal on each antenna. These include thefollowing: the one or two high-speed downlink shared channel (HS-DSCH)data streams 410 which are precoded, using matrix B, at block 420; anumber of dedicated channel signals (e.g., voice and/or control),including voice signals 430, which may be configured using CL-1 transmitdiversity using the matrix V at block 440; and a number of otheroverhead signals 450 (e.g., pilots, voice, control, etc.) that are notprecoded. The precoding matrix V is given by

V=[v₁ v₂ . . . v_(K) _(v) ],   (4)

where K_(v) is the number of dedicated channels configured in CL-1transmit diversity. Each preceding vector (column of V) is given by oneof the 4 possible vectors in the CL-1 codebook.

It is important to note that when the transmitter is configured indual-stream mode, the same set of channelization (spreading) codes isused for both streams. This creates self-interference that must beresolved by the receiver. In other words, when the receiver demodulateseach stream, it must suppress interference from the other stream.

Various embodiments of the present invention exploit a new way ofexpressing the covariance term associated with a given precoded signal.This approach allows the receiver to more readily take into account anarbitrary precoding configuration for the various signals, whilereducing complexity compared to previous solutions and avoidingover-modeling problems.

Consider any one of the transmitted signals (HS-DSCH or dedicatedchannel signal) transmitted according to an arbitrary preceding vectordenoted u=[u₁ u₂]^(T). The “effective” medium channel responsecorresponding to the preceding vector u may be given as:

$\begin{matrix}{{{\hat{g}}^{eff} = {{u_{1}{\hat{g}}_{1}} + {\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}u_{2}{\hat{g}}_{2}}}},} & (5)\end{matrix}$

where ĝ₁ is the channel estimate (medium response) associated withtransmit antenna 1 (Tx1), ĝ₂ is the channel estimate (medium response)associated with transmit antenna 2 (Tx2), γ_(p)(1) is the fraction ofthe total pilot power allocated to antenna 1 (a value between 0 and 1),and γ_(p) (2) is the fraction of the total pilot power allocated toantenna 2 (also a value between 0 and 1).

Next, an impairment term associated with a transmit signal utilizing thepreceding vector u may be denoted R(ĝ^(eff)). The functional form ofR(ĝ^(eff)) was given in the Cairns and Jonsson applications discussedabove, and depends on the pulse shape autocorrelation function as wellas the medium response channel estimates. Elements in this matrixcorrespond to pairs of fingers. For example, for finger f₁ (associatedwith delay d_(f) ₁ and receive antenna l₁) and for finger f₂ (associatedwith delay d_(f) ₂ and receive antenna l₂), the corresponding matrixelement is given by

$\begin{matrix}{{r( {f_{1},f_{2}} )} = {\sum\limits_{p_{1} = 0}^{P - 1}{\sum\limits_{p_{2} = 0}^{P - 1}{{{\hat{g}}^{eff}( {p_{1},l_{1}} )} {( {{\hat{g}}^{eff}( {p_{2},l_{2}} )} )^{*} \cdot {\sum\limits_{\underset{k \neq 0}{k = {- \infty}}}^{\infty}{{x( {d_{f_{1}} - \tau_{p_{1}} - {kT}_{c}} )}{x^{*}( {d_{f_{2}} - \tau_{p_{2}} - {kT}_{c}} )}}}}}}}} & (6)\end{matrix}$

where P is the number of paths, ĝ^(eff) (p,l) is the effective mediumresponse channel coefficient corresponding to the preceding vector uthat is associated with receive antenna l and path delay τ_(p), x(τ) isthe chip pulse shape autocorrelation function, and T_(c) is the chipperiod.

In the Jonsson application, a covariance term R(ĝ^(eff)) is constructedfor each of the four entries in the CL-1 codebook, i.e., u=u_(i) fori=1, 2, 3, 4. Disclosed here is a new form for the impairment term thatis generated as follows. First, substituting Equation (5) into Equation(6) yields the following alternative form for r(f₁, f₂):

$\begin{matrix}{{r( {f_{1},f_{2}} )} = {{{u_{1}}^{2}{r_{11}( {f_{1},f_{2}} )}} + {{u_{2}}^{2}( \frac{\gamma_{p}(1)}{\gamma_{p}(2)} ){r_{22}( {f_{1},f_{2}} )}} + {{{Re}\lbrack {u_{1}u_{2}^{*}} \rbrack}\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}{r_{12}^{+}( {f_{1},f_{2}} )}} + {j\; {{Im}\lbrack {u_{1}u_{2}^{*}} \rbrack}\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}{r_{12}^{-}( {f_{1},f_{2}} )}}}} & (7)\end{matrix}$

where

r ₁₂ ⁺(f ₁ ,f ₂)=r ₁₂(f ₁ ,f ₂)+r* ₁₂(f ₂ ,f ₁)

r ₁₂ ⁻(f ₁ ,f ₂)=r ₁₂(f ₁ ,f ₂)−r* ₁₂(f ₂ ,f ₁)   (8)

Letting m₁ and m₂ each index either transmit antenna 1 or 2, thecovariance term r_(m) ₁ _(m) ₂ (f₁,f₂) appearing in Equations (7) and(8) corresponds to the (m₁,m₂)^(th) pair of Tx antennas, and is givenby:

$\begin{matrix}{{r_{m_{1}m_{2}}( {f_{1},f_{2}} )} = {\sum\limits_{p_{1} = 0}^{P - 1}{\sum\limits_{p_{2} = 0}^{P - 1}{{{\hat{g}}_{m_{1}}( {p_{1},l_{1}} )} {{{\hat{g}}_{m_{2}}^{*}( {p_{2},l_{2}} )} \cdot {\sum\limits_{\underset{k \neq 0}{k = {- \infty}}}^{\infty}{{x( {d_{f_{1}} - \tau_{p_{1}} - {kT}_{c}} )}{x^{*}( {d_{f_{2}} - \tau_{p_{2}} - {kT}_{c}} )}}}}}}}} & (9)\end{matrix}$

where ĝ_(m) ₁ (p₁,l₁) is the channel estimate (medium response)associated with transmit antenna m₁, receive antenna l₁ and path delayτ_(p) ₁ , and ĝ_(m) ₂ (p₂,l₂) is the channel estimate (medium response)associated with transmit antenna m₂, receive antenna l₂ and path delayτ_(p) ₂ .

Equations (7) and (8) thus provide covariance terms corresponding to thethree transmit antenna pairs (1,1), (2,2), and (1,2). Those skilled inthe art will notice that r₁₁ (f₁,f₂) and r₂₂(f₁,f₂) are each a functionof the propagation channel estimates associated with only a singletransmit antenna (Tx1 and Tx2, respectively). In contrast, r₁₂(f₁,f₂) isa function of the channel estimates associated with both transmitantennas.

In what follows, the full matrix consisting of the elements r_(m) ₁ _(m)₂ (f₁,f₂) for all fingers f₁ and f₂ is denoted R_(m) ₁ _(m) ₂ . Usingthis notation, along with Equations (7) and (8), a new form for theimpairment term R(ĝ^(eff)) is given by:

$\begin{matrix}{{{R( {\hat{g}}^{eff} )} = {{{u_{1}}^{2}{R_{11}++}{u_{2}}^{2}( \frac{\gamma_{p}(1)}{\gamma_{p}(2)} )R_{22}} + {{{Re}\lbrack {u_{1}u_{2}^{*}} \rbrack}\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}R_{12}^{+}} + {j\; {{Im}\lbrack {u_{1}u_{2}^{*}} \rbrack}\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}R_{12}^{-}}}},} & (10)\end{matrix}$

where

R ₁₂ ⁺ =R ₁₂ +R ₁₂ ^(H)

R ₁₂ ⁻ =R ₁₂ −R ₁₂ ^(H)   (11)

and R₂ ^(H) denotes the Hermitian transpose of the matrix R₁₂.

Accordingly, rather than constructing the impairment term R(ĝ^(eff)) foru=u_(i) for i=1, 2, 3, 4 using Equation (6), as is shown in the Jonssonreference, instead the three fundamental matrix terms R₁₁, R₂₂, and R₁₂are constructed using Equation (9). This operation only needs to be doneonce (for a given set of channel conditions), since the threefundamental terms are not a function of the preceding weights. In otherwords, the three impairment terms given in Equation (10) are independentof the preceding vector weights.

In order to build an overall impairment covariance matrix which includescontributions from all transmitted signals, it is only necessary toscale and combine these three fundamental terms (comparatively simpleoperations) in different ways for each constituent transmit signal.Thus, the same impairment model terms may be used for any precedingscenario. The scaling/combining takes into account the precoding vectorand power allocation associated with each individual signal.

Accordingly, a new parametric form for the overall impairment covariancematrix associated with a composite transmitted signal is given by:

R=α ₁ R ₁₁ +α ₂ R ₂₂ +α ₁₂ ⁺ R ₁₂ ⁺ +jα ₁₂ ⁻ R ₁₂ ⁻ +βR _(N),   (12)

where R_(N) is an impairment term modeling noise plus other-cellinterference. The associated scaling parameter β captures the energy ofthis impairment.

If first and second HS-DSCH data streams are precoded according tob₁=[b₁₁ b₂₁]^(T) and b₂=[b₁₂ b₂₂]^(T), respectively, and the precodingvector applied to the k^(th) of K voice signals is denoted v_(k)=[v_(1k)v_(2k)]^(T), then the other four scaling parameters in Equation (12) aregiven by:

$\begin{matrix}{\alpha_{1} = {\frac{1}{( {{\gamma_{p}(1)}N_{p}} )}\lbrack {{\Gamma_{D/P}( {{{\gamma_{d}(1)}{b_{11}}^{2}} + {{\gamma_{d}(2)}{b_{12}}^{2}}} )} + {\Gamma_{V/P}{\sum\limits_{k = 1}^{K_{v}}{{\gamma_{v}(k)}{v_{1\; k}}^{2}}}} + {{\gamma_{o}(1)}\Gamma_{O/P}} + {\gamma_{p}(1)}} \rbrack}} & (13) \\{\alpha_{2} = {\frac{1}{{\gamma_{p}(2)}N_{p}}\lbrack {{\Gamma_{D/P}( {{{\gamma_{d}(1)}{b_{21}}^{2}} + {{\gamma_{d}(2)}{b_{22}}^{2}}} )} + {\Gamma_{V/P}{\sum\limits_{k = 1}^{K_{v}}{{\gamma_{v}(k)}{v_{2\; k}}^{2}}}} + {{\gamma_{o}(2)}\Gamma_{O/P}} + {\gamma_{p}(2)}} \rbrack}} & \; \\{\alpha_{12}^{+} = {\frac{1}{N_{p}\sqrt{{\gamma_{p}(1)}{\gamma_{p}(2)}}}\lbrack {{\Gamma_{D/P}( {{{\gamma_{d}(1)}{{Re}\lbrack {b_{11}b_{21}^{*}} \rbrack}} + {{\gamma_{d}(2)}{{Re}\lbrack {b_{12}b_{22}^{*}} \rbrack}}} )} + {\Gamma_{V/P}{\sum\limits_{k = 1}^{K_{v}}{{\gamma_{v}(k)}{{Re}\lbrack {v_{1\; k}v_{2\; k}^{*}} \rbrack}}}}} \rbrack}} & \; \\{\alpha_{12}^{-} = {\frac{1}{N_{p}\sqrt{{\gamma_{p}(1)}{\gamma_{p}(2)}}}\lbrack {{\Gamma_{D/P}( {{{\gamma_{d}(1)}{{Im}\lbrack {b_{11}b_{21}^{*}} \rbrack}} + {{\gamma_{d}(2)}{{Im}\lbrack {b_{12}b_{22}^{*}} \rbrack}}} )} + {\Gamma_{V/P}{\sum\limits_{k = 1}^{K_{v}}{{\gamma_{v}(k)}{{Im}\lbrack {v_{1\; k}v_{2\; k}^{*}} \rbrack}}}}} \rbrack}} & \;\end{matrix}$

where N_(p) represents the spreading factor used for the pilot signals(typically 256), Γ_(x/P) is the ratio of the total power allocated tosignal type-x to the total power allocated to the pilot signals on bothtransmit antennas (x=D for HS-DSCH data, x=V for voice signals and x=Ofor overhead signals), and γ_(x)(k) denotes the fraction of the type-xpower allocated to each user/antenna for pilot signals (x=p), voicesignals (x=v), and overhead signals (x=o). For the case of the datasignals (x=d), γ_(d)(k) represents the allocation of data power betweenstreams; thus, γ_(d)(1)=1 and γ_(d)(2)=0 for single-stream transmission.Typically, γ_(d)(1)=γ_(d)(2)=0.5 for dual-stream transmission, i.e.,energy associated with the data is evenly split between the two streams.

Those skilled in the art will appreciate that the precedingconfiguration of the transmitter is wholly reflected by the scalingparameters α₁, α₂, α₁₂ ⁺, and α₁₂ ⁻ in the above formulation. This meansthat when the preceding configuration changes, e.g., the transmitterswitches between single and dual stream transmission, or when thepreceding vectors used by different voice users changes, only theparameter values change. The overall structure of the receiver in termsof number of impairment terms does not change. Regardless of the actualsignal configuration, the fundamental matrix terms R₁₁, R₂₂, R₁₂, andR_(N) may be constructed in the same manner. (R₁₂ ⁺, and R₁₂ ⁻ areeasily computed from R₁₂). Then, the five corresponding scalingparameters α₁, α₂, α₁₂ ⁺, α₁₂ ⁻, and β may be estimated according toknown techniques (e.g., according to the techniques described in theCairns application discussed above). This same procedure may be usedregardless of the precoding configuration. Thus, the receiver structureis “blind” to the actual preceding used by various signals. The scalingparameter estimates will automatically capture whatever impairment colorexists. Those skilled in the art will appreciate that fewer parametersare estimated, and fewer matrices constructed, compared to the approachdescribed in the Cairns application.

An exemplary method 500 of estimating impairment covariance associatedwith a received composite information signal, in which the modeldescribed above may be used, is thus depicted in FIG. 5. The describedmethod is particularly applicable for processing received signals thatinclude at least one component transmitted from two or more antennasaccording to a precoding vector. However, those skilled in the art willappreciate that the method accommodates non-precoded signals as well.These latter signals are accommodated by the theory underpinning thedescribed impairment model by simply regarding these signals as beingassociated with antenna weighting vectors of [1,0]^(T) or [0,1]^(T).

Method 500 begins at block 510, with the computation of first and secondimpairment model terms, corresponding to first and second transmitantennas respectively. These terms, R₁₁, and R₂₂, are functions ofpropagation channel estimates (typically, medium channel responseestimates) associated with the respective antennas, and further dependon the chip pulse shape autocorrelation function. The elements of R₁₁,and R₂₂ may be calculated according to Equation (9); details forperforming calculations of this general form, including techniques forhandling the infinite summation of Equation (9), are well-known, and aregiven in the Cairns and Jonsson applications discussed above.

At block 520, a third impairment model term, R₁₂, is computed, based onthe propagation channel estimates associated with both transmitantennas. The elements of R₁₂ may also be computed according to Equation(9). Once R₁₂ is calculated, it may be readily decomposed intocomponents R₁₂ ⁺, and R₁₂ ⁻ according to Equation (11).

Similarly, a noise term, R_(N), which represents noise and other-cellinterference, may be calculated, as shown at block 530, according totechniques that are well-known, such as are described in the Cairns andJonsson applications.

At block 540, impairment covariance or data covariance is measured, toprovide a rough estimate, or “snapshot” of signal impairments. Thoseskilled in the art will appreciate that whether impairment covariance ordata covariance is measured will depend on the receiver implementation.Typically, a G-Rake receiver is configured to perform impairmentcovariance measurements using pilot channel data. Measured datacovariance can be obtained by summing outer products of several sampledata vectors (vector elements corresponding to finger outputs), i.e.:

$\begin{matrix}{{{\overset{\sim}{R}}_{meas} = {\sum\limits_{m}{{y(m)}{y^{H}(m)}}}},} & (14)\end{matrix}$

where m is a time index.

At block 550, “instantaneous” values for the parameters that scale theimpairment model terms are determined. Those skilled in the art willappreciate that several of the quantities in Equation (13) are likely tobe unknown. However, values for the scaling parameters may be estimatedby fitting the weighted sum of model terms to the measured covariance(or data covariance, although in this case the fitting equation mustinclude an additional term corresponding to the outer product of the netchannel response). A least-squares or other fitting approach may beused, treating equations with complex quantities as two real-valueequations, as is well known in the art. Once instantaneous values forthe scaling parameters have been determined, they may additionally besmoothed, or filtered, over successive estimates of the scalingparameters values, to reduce estimation noise.

Once values for the scaling parameters have been determined, then theymay be applied to the parametric covariance model terms to generate animpairment covariance estimate, as shown at block 560. If {circumflexover (α)}₁, {circumflex over (α)}₂, {circumflex over (α)}₁₂ ⁺,{circumflex over (α)}₁₂ ⁻ and {circumflex over (β)} represent the fitted(and optionally smoothed) scaling parameter values, then the estimatedimpairment covariance is given by:

{circumflex over (R)} _(u) ={circumflex over (α)} ₁ R ₁₁ +{circumflexover (α)} ₂ R ₂₂ +{circumflex over (α)} ₁₂ ⁺ R ₁₂ ⁺ +j{circumflex over(α)} ₁₂ ⁻ R ₁₂ ⁻ +{circumflex over (β)}R _(N).   (15)

The estimated covariance may be used, for example, for generatingcombining weights for signal detection, to estimate channel quality(e.g., to estimate or predict signal-to-noise-plus-interference or otherchannel quality metric), and so on, as shown at block 570. Periodically,such as at every WCDMA timeslot, the process may be repeated, as shownat block 580, to dynamically adapt the covariance estimate to changingchannel conditions and interference profiles.

As mentioned above, the estimated impairment covariance produced by themethod of FIG. 5 may be used for, among other things, producingprocessing weights, such as combining weights for fingers of a G-RAKEreceiver. The combining weights used for detecting a particular signaldepend on whether one or two streams are being transmitted. Forsingle-stream mode, the combining weights w^(single) may be obtained bysolving the system of equations:

{circumflex over (R)} _(u) w ^(single) =h _(eff)(b),   (16)

where h_(eff)(b) indicates the “effective” net channel coefficients thatdepend on the preceding vector b, and is given by:

$\begin{matrix}{{{h_{eff}(b)} = {{b_{1}h_{1}} + {b_{2}\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}h_{2}}}},} & (17)\end{matrix}$

where h_(m) is the net channel response associated with the m^(th)transmit antenna (m=1 or 2) and b₁,b₂ are the elements of b. Elements ofthe net response vector h_(m) correspond to a given finger. For example,for finger f (associated with delay d_(f) and receive antenna l), thecorresponding vector element is given by:

$\begin{matrix}{{{h_{m}(f)} = {\sum\limits_{p = 0}^{P - 1}{{{\hat{g}}_{m}( {p,l} )}{x( {d_{f} - \tau_{p}} )}}}},} & (18)\end{matrix}$

where P is the number of paths, ĝ_(m)(p,l) is the channel estimate(medium response) associated with transmit antenna m, receive antenna land path delay τ_(p), and x(τ) is the chip pulse shape autocorrelationfunction. (Those skilled in the art will appreciate that b here is thefirst column of B as described above for single stream mode. Thoseskilled in the art will also appreciate that the same approach appliesto signals transmitted from a single antenna, in that b in this caseequals [1,0]^(T) or [0,1]^(T).)

In dual-stream mode, on the other hand, two data streams are transmittedsimultaneously, both sharing the same set of channelization codes in aWCDMA system. This creates additional cross-stream interference (inWCDMA systems, this may be referred to as code-reuse interference),which affects the impairment covariance as related to each of thesimultaneously transmitted streams.

This cross-stream interference may be accounted for by adding anadditional term to the parametric covariance R formulation in Equation(1). For the case of a first data stream (stream 1), the cross-streaminterference is due to the second stream (stream 2), thus the impairmentcovariance matrix is given by:

R _(stream1) =R+α _(PC)(2)h _(eff)(b ₂)h _(eff) ^(H)(b ₂).   (19)

Similarly, for the case of stream 2, the code-reuse interference is dueto stream 1, thus the covariance matrix is given by:

R _(stream2) =R+α _(PC)(1)h _(eff)(b ₁)h _(eff) ^(H)(b ₁).   (20)

In these expressions, the quantities α_(PC)(1) and α_(PC)(2) are thenormalized per-code energies per-symbol allocated to stream 1 and 2,respectively. Letting n index streams, the per-code energy for the nthstream is given by:

$\begin{matrix}{{{\alpha_{PC}(n)} = {( \frac{1}{{\gamma_{p}(1)}N_{p}} )( \frac{N_{s}}{K} ){\gamma_{d}(n)}\Gamma_{D/P}}},} & (21)\end{matrix}$

where N_(s) is the spreading factor used for HS-DSCH (typically 16), andK is the number of spreading codes used for each data stream (same forboth streams). The quantity h_(eff)(b_(n)) is the effective net responsevector corresponding to the n^(th) stream and is given by:

$\begin{matrix}{{h_{eff}( b_{n} )} = {{b_{1\; n}h_{1}} + {p_{2\; n}\sqrt{\frac{\gamma_{p}(1)}{\gamma_{p}(2)}}{h_{2}.}}}} & (22)\end{matrix}$

FIG. 6 illustrates a procedure for obtaining a stream-specific estimateof the impairment covariance in a dual-stream scenario, given thepreceding stream-specific model. At block 160, a generalized impairmentcovariance, such as according to Equation (15) is estimated. Thisgeneralized impairment covariance, which may be obtained according tomethod 500 illustrated in FIG. 5, is directly applicable to thesingle-stream scenario as described above. At block 610, a termcapturing the cross-stream interference to stream 1 caused by stream 2is computed, based on the per-code energy for stream 2 and the effectivenet response for stream 2, which in turn depends upon the antennaweighting vector (preceding vector) corresponding to stream 2. Finally,at block 630, a stream-specific impairment covariance estimate iscomputed, e.g., according to Equation (19).

The stream-specific impairment covariance estimates {circumflex over(R)}_(stream1) and {circumflex over (R)}_(stream2) may be used, forexample, to compute combining weights for detecting the first and seconddata streams, according to:

{circumflex over (R)} _(stream1) w ₁ ^(dual) =h _(eff)(b ₁)

{circumflex over (R)} _(stream2) w ₂ ^(dual) =h _(eff)(b ₂)   (23)

Similarly, the stream-specific impairment covariance estimates, alongwith the corresponding effective net channel responses, may be used tocompute stream-specific signal-to-interference-plus-noise ratios (SINRs)for each of the data streams, according to well-known techniques. TheseSINRs may be used, for example, to report channel quality information tothe serving base station.

In an alternative approach, stream-specific Minimum-Mean-Squared-Error(MMSE) combining weights may be calculated from an estimated datacovariance matrix. The data covariance matrix is computed from theimpairment covariance estimate described above and cross-streaminterference terms for each of the first and second data streams. Thus:

R _(d) ={circumflex over (R)}+α _(PC)(1)h _(eff)(b ₁)h _(eff) ^(H)(b₁)+α_(PC)(2)h _(eff)(b ₂)h _(eff) ^(H)(b ₂)   (24)

The estimated data covariance matrix may then be used to computestream-specific combining weights according to:

R _(d) w ₁ ^(dual) =h _(eff)(b ₁)

R _(d) w ₂ ^(dual) =h _(eff)(b ₂).   (25)

The techniques described above may be adapted slightly to facilitate theestimation of SINR or other channel quality metrics for signaltransmission scenarios other than a scenario currently employed, i.e.for a projected transmission scenario. Similarly, the techniques may beused to estimate SINR for projected transmission scenarios even when themobile station is not scheduled for downlink transmissions at all. Forexample, R_(stream1) and R_(stream2) as estimated above correspond to aparticular active transmission scenario (dual-stream) using a particularset of preceding vectors (b₁ and b₂). For Channel Quality Indicator(CQI) estimation purposes, the receiver may typically consider a numberof transmitter modes that differ from the currently used mode. Forinstance, the current mode may be one of the four possible dual-streammodes shown in Equation (3); however, the receiver must also prepare aCQI report for one or more of the four possible single-stream modesshown in Equation (2). Such a requirement occurs in Release 7 of theUMTS standard, where the user equipment (UE) is required to periodicallyprepare single-stream CQI reports (referred to as Type B reports),regardless of what the UE's current or preferred mode is.

In preparing such CQI estimates, the receiver typically forms aparametric estimate of the impairment covariance matrix. Since thescaling parameters α₁, α₂, α₁₂ ⁺, and α₁₂ ⁻ defined for the impairmentcovariance model above are a function of the transmission mode (single-or dual-stream, as well as the particular precoding matrix B employed),it is not accurate to simply use the estimated impairment covarianceestimated above. If the mode for which the CQI estimate is beingprepared differs from the current mode, then a mismatch would occur.

There are several possible approaches to estimating impairmentcovariance for transmission scenarios other than a currently activescenario. FIG. 7 illustrates a procedure for forming an impairmentcovariance matrix for an arbitrary desired mode; the resultingimpairment covariance matrix may be used according to well-knowntechniques to produce the corresponding CQI information.

The procedure begins at block 710, with determining the transmitter modeinformation for the desired mode. This could be obtained, for example,from a look-up table stored in the UE that contains information aboutall possible transmitter modes. This transmitter mode informationspecifies, for example, the number of data streams and the precodingmatrix (B).

At block 720, additional transmitter mode information is obtained,whether by hypothesis, estimation, use of nominal values, or by explicithigher layer signaling from the base station. This additionaltransmitter mode information may include: the number of spreading codes(K) employed; as well as the data-to-pilot power ratio (Γ_(D/P)),voice-to-pilot power ratio (Γ_(V/P)), and overhead-to-pilot power ratio(Γ_(O/P)). The additional transmitter mode information may furtherinclude the preceding matrix (V) for voice users, and the voice powerdistribution (γ_(v)(i)). This information in particular may not be knownto the UE in detail, hence nominal values could be used. In oneembodiment, the UE may assume a precoding matrix with all 4 possibleentries in the CL-1 codebook, i.e., V=[u₁ u₂ u₃ u₄] and equal powerdistribution amongst 4 virtual voice users, i.e., γ_(v)(i)=1/4 for i=1,2, 3, 4. Finally, the distribution of data power across streams(γ_(s)(1) and γ_(s)(2)), distribution of pilot power across antennas(γ_(p)(1) and γ_(p)(2)), and spreading factor for pilot channel (N_(p))and data channel (N_(s)) must be known, estimated, or hypothesized.

Next, at block 730, channel estimates (ĝ_(m)) corresponding to eachtransmit antenna are obtained. At block 740, these channel estimates areused to compute the impairment model terms R₁₁, R₂₂, and R₁₂, usingEquation (9) above, R₁₂ ⁺ and R₁₂ ⁻, using Equation (11), and R_(N).

At block 750, values for α₁, α₂, α₁₂ ⁺ and α₁₂ ⁻ are calculated usingEquation (13) and the information obtained at blocks 710 and 720. Avalue for β may be obtained from some alternate means. For example, avalue estimated for β based on a previous slot or number of slots (e.g.,smoothed value) may be used. Alternatively, if the UE is currentlydemodulating the HS-DSCH configured in a different mode than the one forwhich a CQI estimate is being prepared, the same β could be used forboth demodulation and CQI. This is accurate, since the value of β is notmode-specific.

At block 760, a parametric estimate of impairment covariance matrix({circumflex over (R)}_(u)) is computed, using the terms computed atblock 740 and the parameters calculated at block 750, using Equation(12). If desired mode is single-stream mode, then estimation of theimpairment covariance is complete. Otherwise, as shown at block 770, theprocess is continued to compute stream-specific impairment covariances.

Accordingly, at block 780, effective net response vectors h_(eff)(b_(n))for each stream are computed, as in Equation (22), and per-code energies(α_(PC)(n)) corresponding to each stream are computed, as in Equation(21). Finally, at block 790, parametric estimates of impairmentcovariance for each data stream ({circumflex over (R)}_(stream1) and{circumflex over (R)}_(stream2)) are calculated, using Equations (19)and (20).

In some cases, some of the terms needed to calculate the scalingparameters may be unknown, and might be unsusceptible to readyestimation. For instance, detailed information about the distribution ofvoice channels across the transmit antennas may be unknown. In someembodiments, then, fitting parameters estimated for the current mode(e.g., according to the method of FIG. 5), may be reused in order toform the impairment covariance matrix corresponding to the potentiallydifferent mode(s) for which the UE is preparing a CQI estimate. In theseembodiments, antenna weighting vectors corresponding to the projectedtransmission scenario may be used to compute effective net channelresponse estimates and/or stream-specific impairment covarianceestimates. In some scenarios, this shortcut incurs only a very minornegative impact on performance, while offering a significant reductionin complexity.

In yet another embodiment, if scaling parameters have been fitted for acurrently scheduled scenario, then effects of the transmission scenariomay be “backed out” of the scaling parameter estimates, and newestimates formed based on a projected transmission scenario. Thoseskilled in the art will appreciate, upon scrutinizing the scalingparameter expressions in Equation (13) that if the currently usedscheduling matrix (B), the overall data-to-pilot code energy ratio(Γ_(D/P)), the allocation of power between data streams (γ_(d)), theallocation of pilot power between the transmit antennas (γ_(d)), and thepilot spreading factor (N_(p)) are known for the current scenario, theneach scaling parameter may be re-estimated by removing the impact of thecurrent antenna weighting vector and substituting a weighting vector forthe projected scenario. If the mobile receiver is currently scheduledfor downlink transmissions, then each of these terms is generally knownto the mobile receiver or readily estimable using known techniques.

Thus, in some embodiments of a method for estimating SINR for projectedtransmission scenarios (i.e., scenarios other than a currently scheduledtransmission scenario), a mobile receiver may model impairmentcovariance, according to the invention, and fit the scaling parametersto measured data. The resulting fitting parameters will reflect theactual preceding vector(s). Using an estimated data/pilot energy ratio,and known values for the stream/antenna allocation ratios and the actualpreceding vectors, the mobile receiver may then re-calculate new scalingparameters from the fitted parameters, using pre-coding vector(s) for aprojected precoding scenario. After computing an estimate of impairmentcovariance for this projected scenario based on the re-calculatedscaling parameters, the mobile receiver may then compute SINR using thenew estimate—this will reflect SINR for the projected precodingscenario. This procedure may be repeated as necessary for additionalscenarios.

Although described herein in terms of own-cell interference, the presentinvention may be applied using model terms to model other-cellinterference. For example, a single other-cell covariance term perdominant interfering base station may be added to equation (1), asdescribed in the Cairns application. Additionally or alternatively,multiple covariance terms may be added to a count for transmit diversitybeing used in other cells. Soft handoff may be handled, again asdescribed in the Cairns application. Although described herein in termsof downlink reception, the present invention may be applied in theuplink as well.

Embodiments of the present invention thus provide improved interferencesuppression for both symbol-level (G-Rake) and chip-level (chipequalizer) LIW receivers, which are the two main architectures foradvanced receivers in WCDMA systems. Those skilled in the art willappreciate that the particular design of a LIW receiver in accordancewith the inventive techniques, and the associated nomenclature used inconnection with such a receiver, may vary according to the networkstandard involved, but such variations are not germane to understandingor explaining the present invention. Moreover, it should be understoodthat the networks and radio devices illustrated and discussed herein aresimplified; actual implementations likely will have additional entitiesomitted herein for clarity.

Nevertheless, an exemplary mobile terminal 112 includes one or both ofthe exemplary receiver circuits 200 or 300, illustrated in FIGS. 2 and3, respectively. These receiver circuits may be implemented usingvarious processing circuits, including A/D converters, filters, DSPs orother digital processors, memory, and the like. In at least oneexemplary embodiment, mobile terminal 112 includes one or more DSPsand/or Application Specific Integrated Circuts (ASICS) or otherprogrammable devices to implement receiver 112 including a G-Rakereceiver as illustrated in FIG. 2. The processing circuits may beconfigured to include processing logic to carry out one or more of themethods described herein. It should thus be understood that at least aportion of the present invention's functionality may be embodied asstored computer instructions in the form of micro-code, firmware,software, etc.

More generally, the present invention can be implemented in hardware,software, or essentially any combination thereof, according to the needsof a particular design. Although the present invention has beendescribed herein with respect to particular features, aspects andembodiments thereof, it will be apparent that numerous variations,modifications, and other embodiments are possible within the broad scopeof the present invention, and accordingly, all variations, modificationsand embodiments are to be regarded as being within the scope of theinvention. The present embodiments are therefore to be construed in allaspects as illustrative and not restrictive and all changes comingwithin the meaning and equivalency range of the appended claims areintended to be embraced therein.

1. A method of estimating impairment covariance associated with areceived composite information signal comprising at least a first datastream transmitted from first and second antennas according to a firstantenna weighting vector, the method comprising: constructing animpairment model including one or more model terms scaled bycorresponding scaling parameters, wherein the model terms capturepropagation channel effects and are independent of the first antennaweighting vector, and wherein the scaling parameters capture effects ofthe first antenna weighting vector; and computing a parametric estimateof the impairment covariance using the impairment model.
 2. The methodof claim 1, wherein constructing an impairment model comprises computinga first impairment model term as a function of first propagation channelestimates corresponding to the first antenna, a second impairment modelterm as a function of second propagation channel estimates correspondingto the second antenna, and a third impairment model term as a functionof both the first and second propagation channel estimates, wherein eachof the first, second, and third impairment model terms are independentof the first antenna weighting vector.
 3. The method of claim 2, whereinconstructing an impairment model further comprises measuring impairmentcovariance or data covariance associated with the received compositeinformation signal and estimating values for first, second, and thirdscaling parameters corresponding to the first, second, and thirdimpairment model terms using the measured impairment covariance or datacovariance and the computed first, second, and third impairment modelterms.
 4. The method of claim 2, wherein constructing an impairmentmodel further comprises computing a fourth impairment model term as afunction of both the first and second propagation channel estimates anda fifth impairment model term corresponding to noise, and estimatingvalues for first, second, third, fourth, and fifth scaling parameterscorresponding to the first, second, third, fourth, and fifth impairmentmodel terms using the measured impairment covariance or data covarianceand the computed first, second, third, fourth, and fifth impairmentmodel terms.
 5. The method of claim 1, further comprising calculatingprocessing weights as a function of the parametric estimate of theimpairment covariance and propagation channel estimates corresponding tothe first and second antennas.
 6. The method of claim 5, wherein theprocessing weights comprise combining weights for use in a symbol-levelequalizer.
 7. The method of claim 5, wherein the processing weightscomprise filter weights for use in a chip-level equalizer.
 8. The methodof claim 1, further comprising calculating asignal-to-interference-plus-noise ratio (SINR) estimate as a function ofthe parametric estimate of the impairment covariance.
 9. The method ofclaim 1, further comprising calculating a projected impairmentcovariance estimate based on at least a second antenna weighting vectorcorresponding to a projected transmitted signal configuration.
 10. Themethod of claim 9, wherein calculating a projected impairment covarianceestimate comprises revising the scaling parameters based on the secondantenna weighting vector and calculating the projected impairmentcovariance estimate based on the revised scaling parameters.
 11. Themethod of claim 1: wherein the composite information signal comprises asecond data stream transmitted from both the primary and secondarytransmit antennas according to a second antenna weighting vector;wherein constructing an impairment model comprises constructing across-stream interference term corresponding to the second data streamas a function of the second antenna weighting vector and propagationchannel estimates corresponding to the first and second antennas; andwherein computing a parametric estimate of the impairment covariancecomprises computing a stream-specific estimate of the impairmentcovariance for the first data stream based on the impairment model andthe cross-stream interference term corresponding to the second datastream.
 12. The method of claim 11, further comprising calculatingstream-specific combining weights as a function of the stream-specificestimate of the impairment covariance, for use in symbol-levelequalization and detection of the first data stream.
 13. The method ofclaim 11, further comprising calculating a stream-specificsignal-to-interference-plus-noise ratio (SINR) as a function of thestream-specific estimate of the impairment covariance.
 14. The method ofclaim 1, wherein the composite information signal comprises a seconddata stream transmitted from both the primary and secondary transmitantennas according to a second antenna weighting vector, and wherein theconstructed impairment model omits any cross-stream interference terms,the method further comprising: computing a data covariance estimate as afunction of the computed parametric estimate of the impairmentcovariance, a first cross-stream interference term corresponding to thefirst data stream, and a second cross-stream interference correspondingto the second data stream; and calculating stream-specific combiningweights for the first data stream as a function of the data covarianceestimate and an effective net channel response corresponding to thefirst data stream.
 15. A wireless communication receiver, comprising: aradio front-end circuit configured to receive a composite informationsignal, the composite information signal comprising at least a firstdata stream transmitted from first and second transmit antennasaccording to a first antenna weighting vector; and one or moreprocessing circuits configured to: construct an impairment modelincluding one or more model terms scaled by corresponding scalingparameters, wherein the model terms capture propagation channel effectsand are independent of the first antenna weighting vector, and whereinthe scaling parameters capture effects of the first antenna weightingvector; and compute a parametric estimate of the impairment covarianceusing the impairment model
 16. The wireless communication receiver ofclaim 15, wherein the one or more processing circuits are configured toconstruct an impairment model by computing a first impairment model termas a function of first propagation channel estimates corresponding tothe first antenna, a second impairment model term as a function ofsecond propagation channel estimates corresponding to the secondantenna, and a third impairment model term as a function of both thefirst and second propagation channel estimates, wherein each of thefirst, second, and third impairment model terms are independent of thefirst antenna weighting vector.
 17. The wireless communication receiverof claim 16, wherein the one or more processing circuits are furtherconfigured to construct an impairment model by measuring impairmentcovariance or data covariance associated with the received compositeinformation signal and estimating first values for first, second, andthird scaling parameters corresponding to the first, second, and thirdimpairment model terms using the measured impairment covariance or datacovariance and the computed first, second, and third impairment modelterms.
 18. The wireless communication receiver of claim 15, wherein theone or more processing circuits are further configured to calculateprocessing weights as a function of the parametric estimate of theimpairment covariance and propagation channel estimates corresponding tothe first and second antennas.
 19. The wireless communication receiverof claim 18, wherein the one or more processing circuits comprise asymbol-level equalizer, and wherein the processing weights comprisecombining weights for use in the symbol-level equalizer.
 20. Thewireless communication receiver of claim 15, wherein the one or moreprocessing circuits are further configured to calculate asignal-to-interference-plus-noise ratio (SINR) estimate as a function ofthe parametric estimate of the impairment covariance.
 21. The wirelesscommunication receiver of claim 15, wherein the one or more processingcircuits are further configured to calculate a projected impairmentcovariance estimate based on at least a second antenna weighting vectorcorresponding to a projected transmitted signal configuration.
 22. Thewireless communication receiver of claim 21, wherein the one or moreprocessing circuits are configured to calculate the projected impairmentcovariance estimate by revising the scaling parameters based on thesecond antenna weighting vector and calculating the projected impairmentcovariance estimate based on the revised scaling parameters.
 23. Thewireless communication receiver of claim 15, wherein the compositeinformation signal comprises a second data stream transmitted from boththe primary and secondary transmit antennas according to a secondantenna weighting vector, wherein the one or more processing circuitsare configured to: construct the impairment model by constructing across-stream interference term corresponding to the second data streamas a function of the second antenna weighting vector and propagationchannel estimates corresponding to the first and second antennas; andcompute a parametric estimate of the impairment covariance by computinga stream-specific estimate of the impairment covariance for the firstdata stream based on the impairment model and the cross-streaminterference term corresponding to the second data stream.
 24. Thewireless communication receiver of claim 23, further comprising asymbol-level equalizer, wherein the one or more processing circuits arefurther configured to calculate stream-specific combining weights as afunction of the stream-specific estimate of the impairment covariance,for use in symbol-level equalization and detection of the first datastream.
 25. The wireless communication receiver of claim 15, wherein thecomposite information signal comprises a second data stream transmittedfrom both the primary and secondary transmit antennas according to asecond antenna weighting vector, and wherein the constructed impairmentmodel omits any cross-stream interference terms, and wherein the one ormore processing circuits are further configured to: compute a datacovariance estimate as a function of the computed parametric estimate ofthe impairment covariance, a first cross-stream interference termcorresponding to the first data stream, and a second cross-streaminterference corresponding to the second data stream; and calculatestream-specific combining weights for the first data stream as afunction of the data covariance estimate and an effective net channelresponse corresponding to the first data stream.